what is wizzie?

Wizzie is built upon the same technologies used in data-driven companies like Facebook, eBay or LinkedIn, extended and enhanced by our engineers.

All components are based on micro-services that can be scaled based on capacity demand, while maintaining proper data isolation and perdurance. At the same time, we have kept the complexity to the minimum by using a reduced toolset.

Our Technology

Our technology lineup consists of Open Source platforms and technologies that guarantee and simplify the integration with existing systems of almost any kind. This makes our ecosystem not only future-proof, but also allows the investments in technology you’ve already made to have a better ROI, to because all of these technologies are going to be around in the near and mid-term.

Kafka

Kafka is by far the most common technology in real-time big data. Used by all Internet-scale companies in the world, it provides extreme performance through the concept of partitions, and security through replication.

We use Kafka Connect to build our data bridges and Kafka Streams for event processing, maintaining complexity low, as both are just libraries of Kafka without adding more pieces.

Kubernetes

Kubernetes is an open-source platform designed to automate deploying, scaling, and operating application containers based on docker. We use Kubernetes to deploy applications quickly and predictably, scale applications on the fly, roll out new features seamlessly and limit hardware usage to required resources only.

Wizzie runs on any Kubernetes compatible environment, including cloud and bare metal.

Druid

Druid is an open-source data store designed for sub-second queries on real-time and historical data. It is primarily used for business intelligence (OLAP) queries on event data. Druid provides low latency (real-time) data ingestion, flexible data exploration, and fast data aggregation.

We use Druid as our primary or even solely tiered data store, taking advantage of its flexibility, speed, and scalability.

H2O.ai

Using complex multi-layer artificial neural networks, H2O helps us derive insights from large unstructured data such as logs or structured data such as time series. Open-source Deep Learning frameworks such as TensorFlow, MXNet, and Caffe are optimized for fast training of such models using GPUs. GPUs excel at massively parallel workloads and speed up neural network training by 10-75x compared to conventional CPUs.

The Wizzie Ecosystem

The Wizzie ecosystem is built upon two main components: Prozzie and the Wizzie Data Platform (WDP). This ecosystem allows you to collect, process and index any type of data, from any source you can imagine.

Prozzie

Prozzie provides the different data source collection and transformation functions, has fault-tolerance, is data persistent, and offers back-pressure and data authentication as well as encryption from Prozzie to the platform.

The connectors, which perform the actual data collection, are managed by the Prozzie. These connectors can process almost any current protocol, albeit the generic ones (like HTTP, MQTT, KAFKA…) or specific ones (like NetFlow, IoT, Syslog…).

 

Wizzie Data Platform (WDP)

The ecosystem’s cornerstone is the Wizzie Data Platform (WDP). The platform is based on a Big Data stack that is entirely deployed on top of a microservices architecture, providing scalability, elasticity, and modularization.

WDP is a continuous data stream where the Wizzie Workflow module (see below) performs the processing. This scenario allows building different solutions on top of the platform. You can deploy many solutions on top of the base platform but, in case you need something special, you can use plugins which extend the WDP capabilities even beyond. Also, WDP is fully managed through different API REST services.

The Wizzie workflow

The Wizzie Workflow module is what separates our ecosystem from the rest. Not only does is gather all the necessary data from all the available sensors and information sources, but it processes the stream in real time to normalize the data format, enrich and correlate the information combining various data sources and finally index the data to allow a faster retrieval when needed.

WDP Editions

The Wizzie Data Platform is available in three different flavours: DATA PLATFORM, DATA CLOUD (available soon) and OEM EDITION. If you don’t know which one could be right for you, here are short descriptions of all three:

  • DATA PLATFORM: This is our main platform. A managed environment, easily deployable, with automatic scalability and a very high tolerance to failures, to be always available fr your data processing and visualization needs.
  • DATA CLOUD: Very soon you will be able to enjoy all the power of the Wizzie Data Platform directly from the cloud. Conveniente, scalable, agnostic. WDP at its best.
  • OEM EDITION: If you are thinking about developing a new product or service and need to accelerate the time-to-market and focus on your core business, the OEM version of WDP is the most flexible of all and can be adapted to match the exact needs of your projects and/or customers.

Wizzie Data Platform

  • Easy deployment: Kubernetes
  • Multi-tenant
  • Open-source + proprietary components
  • High scalability
  • Smart resilience
  • Management services
  • Supports plugins
  • Premium support
  • Private Install: Amazon Web Services, Google Cloud Platform, On-Premise

Wizzie OEM Edition

If you need a particular customization of our platform, we also have an OEM Edition that can be tailored to your exact needs. Please contact us with your specifications, and we will find the best solution.

Wizzie Data Cloud

Every edition includes

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Real Time Processing

Put the power of Big Data at your fingertips with Wizzie.
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Open components

Future proof technology, supported by the Community.

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Data Normalization

Use as many data sources as you like. Wizzie unifies them.
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Protocol connectors

Don’t worry about your data acquisition or the protocols.

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Growing capacity

Start small, grow over time. Wizzie scales easily.
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Modular architecture

Change or add what you need for your use cases.

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